Generating spectral responses of materials

ABSTRACT

In one non-limiting embodiment, the present disclosure is directed to a controller having a memory; and a processor coupled to the memory and configured to: cause a neural network to receive current measurements of a current material; instruct the neural network to determine dominant features of the current measurements; instruct the neural network to provide the dominant features to a decoder; and instruct the decoder to generate a generated spectral response of the current material based on the dominant features. In another non-limiting embodiment, the present disclosure is directed to a method including the steps of receiving current measurements of a current material; determining dominant features of the current measurements; providing the dominant features; and generating a generated spectral response of the current material based on the dominant features.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional patent applicationNo. 62/532,803 filed on Jul. 14, 2017 by The Board of Regents of theUniversity of Oklahoma and titled “Generating Spectral Responses ofMaterials,” U.S. provisional patent application No. 62/554,352 filed onSep. 5, 2017 by The Board of Regents of the University of Oklahoma andtitled “Generating Spectral Responses of Materials,” and U.S.provisional patent application No. 62/598,182 filed on Dec. 13, 2017 byThe Board of Regents of the University of Oklahoma and titled“Generating Spectral Responses of Materials,” all of which areincorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

Material analyses are important tools in many industries. Materialanalyses help determine types, characterizations, properties, andpositions of those materials, as well as what substances and how much ofthose substances are in those materials. The properties include spatialfeatures, internal arrangements, compositions, structures,distributions, and temporal changes. It is desirable to conduct materialanalyses in a cost-effective and operationally-convenient manner in theabsence of the infrastructure needed to directly perform those materialanalyses. As a result, significant research is directed to reducingcosts of material analyses, as well as improving materials analyses withan emphasis on reducing operational challenges.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic diagram of a system for generating spectralresponses of materials.

FIG. 2 is a flowchart illustrating a method of generating a spectralresponse of a material according to an embodiment of the disclosure.

FIG. 3 is a schematic diagram of the trainer in FIG. 1.

FIG. 4 is a 2D latent space according to an embodiment of thedisclosure.

FIG. 5 is a 3D latent space according to an embodiment of thedisclosure.

FIG. 6 is a schematic diagram of mathematical operations performed in aVAE according to an embodiment of the disclosure.

FIG. 7 is a schematic diagram of the tester in FIG. 1.

FIG. 8 is a schematic diagram of the generator in FIG. 1.

FIG. 9 is a flowchart illustrating a method of generating a generatedspectral response of a material according to another embodiment of thedisclosure.

FIG. 10 is a schematic diagram of a device according to an embodiment ofthe disclosure.

FIG. 11 is a plot comparing historical spectral responses to testedspectral responses for the 2D latent space.

FIG. 12 is a plot of R-squared for the generated spectral responses forthe 2D latent space.

FIG. 13 is a plot comparing historical spectral responses to testedspectral responses for the 3D latent space.

FIG. 14 is a plot of R-squared for the generated spectral responses forthe 3D latent space.

FIG. 15 is a series of plots comparing original dielectric spectra togenerated dielectric spectra.

FIG. 16 is a GUI for implementing the system in FIG. 1 according to anembodiment of the disclosure.

FIG. 17 is a schematic diagram of a generator based on a VAE-CNN modelaccording to an embodiment of the disclosure.

FIG. 18 is a schematic diagram of a generator based on an LSTM/RNN modelaccording to an embodiment of the disclosure.

FIG. 19 is a schematic diagram of a generator based on a GAN modelaccording to an embodiment of the disclosure.

FIG. 20 is a flowchart illustrating a method of using a stacked ANNmodel for generating dielectric spectra according to an embodiment ofthe disclosure.

DETAILED DESCRIPTION

In at least one non-limiting embodiment, the present disclosure isdirected to a controller having a memory, and a processor coupled to thememory and configured to cause a neural network to (1) receive currentmeasurements of a current material, (2) instruct the neural network todetermine dominant features of the current measurements, (3) instructthe neural network to provide the dominant features to a decoder, and(4) instruct the decoder to generate a specific spectral response of thecurrent material based on the dominant features.

In another non-limiting embodiment, the disclosure is directed to amethod including the steps of (1) receiving current measurements of acurrent material, (2) determining dominant features of the currentmeasurements, (3) providing the dominant features, and (4) generating aspectral response of the current material based on the dominantfeatures.

In another non-limiting embodiment, the present disclosure is directedto a system having (1) a trainer including a deep neural network modelhaving an encoder, a latent space database coupled to the encoder, and afirst decoder; (2) a neural network system having a first neuralnetwork, and a second decoder associated with the first decoder; (3) atester having a second neural network associated with the first neuralnetwork; (4) a third decoder associated with the second decoder; (5) agenerator having a third neural network associated with the secondneural network; and (6) a fourth decoder associated with the thirddecoder. In non-limiting embodiments, the deep neural network model canbe a VAE, a GAN, an LSTM, an RNN, or a CNN for generating the specificspectral response.

The spectral response generated by the controllers, systems, and methodsof the present disclosure may be used to instruct an operation such as,but not limited to, a drilling operation and/or a well completionoperation.

It should be understood at the outset that, although an illustrativeimplementation of one or more embodiments are provided below, thedisclosed systems and/or methods may be implemented using any number oftechniques, whether currently known or in existence. The disclosureshould in no way be limited to the illustrative implementations,drawings, and techniques illustrated below, including the exemplarydesigns and implementations illustrated and described herein, but may bemodified in accordance with the presently disclosed inventive concepts.

Features of any of the embodiments described herein may be combined withany of the other embodiments to create a new embodiment. These and otherfeatures will be more clearly understood from the following detaileddescription taken in conjunction with the accompanying drawings andexemplary claims.

Before describing various embodiments of the present disclosure in moredetail by way of exemplary description, examples, and results, it is tobe understood as noted above that the present disclosure is not limitedin application to the details of methods and apparatus as set forth inthe following description. The present disclosure is capable of otherembodiments or of being practiced or carried out in various ways. Assuch, the language used herein is intended to be given the broadestpossible scope and meaning; and the embodiments are meant to beexemplary, not exhaustive. Also, it is to be understood that thephraseology and terminology employed herein is for the purpose ofdescription and should not be regarded as limiting unless otherwiseindicated as so. Moreover, in the following detailed description,numerous specific details are set forth in order to provide a morethorough understanding of the disclosure. However, it will be apparentto a person having ordinary skill in the art that the embodiments of thepresent disclosure may be practiced without these specific details. Inother instances, features which are well known to persons of ordinaryskill in the art have not been described in detail to avoid unnecessarycomplication of the description.

Unless otherwise defined herein, scientific and technical terms used inconnection with the present disclosure shall have the meanings that arecommonly understood by those having ordinary skill in the art. Further,unless otherwise required by context, singular terms shall includepluralities and plural terms shall include the singular.

All patents, published patent applications, and non-patent publicationsmentioned in the specification are indicative of the level of skill ofthose skilled in the art to which the present disclosure pertains. Allpatents, published patent applications, and non-patent publicationsreferenced in any portion of this application are herein expresslyincorporated by reference in their entirety to the same extent as ifeach individual patent or publication was specifically and individuallyindicated to be incorporated by reference.

As utilized in accordance with the methods and apparatus of the presentdisclosure, the following terms, unless otherwise indicated, shall beunderstood to have the following meanings:

The use of the word “a” or “an” when used in conjunction with the term“comprising” in the claims and/or the specification may mean “one,” butit is also consistent with the meaning of “one or more,” “at least one,”and “one or more than one.” The use of the term “or” in the claims isused to mean “and/or” unless explicitly indicated to refer toalternatives only or when the alternatives are mutually exclusive,although the disclosure supports a definition that refers to onlyalternatives and “and/or.” The use of the term “at least one” will beunderstood to include one as well as any quantity more than one,including but not limited to, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30,40, 50, 100, or any integer inclusive therein. The term “at least one”may extend up to 100 or 1000 or more, depending on the term to which itis attached; in addition, the quantities of 100/1000 are not to beconsidered limiting, as higher limits may also produce satisfactoryresults. In addition, the use of the term “at least one of X, Y and Z”will be understood to include X alone, Y alone, and Z alone, as well asany combination of X, Y and Z.

As used herein, all numerical values or ranges include fractions of thevalues and integers within such ranges and fractions of the integerswithin such ranges unless the context clearly indicates otherwise. Thus,to illustrate, reference to a numerical range, such as 1-10 includes 1,2, 3, 4, 5, 6, 7, 8, 9, 10, as well as 1.1, 1.2, 1.3, 1.4, 1.5, etc.,and so forth. Reference to a range of 1-50 therefore includes 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc., upto and including 50, as well as 1.1, 1.2, 1.3, 1.4, 1.5, etc., 2.1, 2.2,2.3, 2.4, 2.5, etc., and so forth. Reference to a series of rangesincludes ranges which combine the values of the boundaries of differentranges within the series. Thus, to illustrate reference to a series ofranges, for example, of 1-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-75,75-100, 100-150, 150-200, 200-250, 250-300, 300-400, 400-500, 500-750,750-1,000, includes ranges of 1-20, 10-50, 50-100, 100-500, and500-1,000, for example. A reference to degrees such as 1 to 90 isintended to explicitly include all degrees in the range.

As used herein, the words “comprising” (and any form of comprising, suchas “comprise” and “comprises”), “having” (and any form of having, suchas “have” and “has”), “including” (and any form of including, such as“includes” and “include”) or “containing” (and any form of containing,such as “contains” and “contain”) are inclusive or open-ended and do notexclude additional, unrecited elements or method steps.

The term “or combinations thereof” as used herein refers to allpermutations and combinations of the listed items preceding the term.For example, “A, B, C, or combinations thereof” is intended to includeat least one of: A, B, C, AB, AC, BC, or ABC, and if order is importantin a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB.Continuing with this example, expressly included are combinations thatcontain repeats of one or more item or term, such as BB, AAA, AAB, BBC,AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan willunderstand that typically there is no limit on the number of items orterms in any combination, unless otherwise apparent from the context.

Throughout this application, the term “about” is used to indicate that avalue includes the inherent variation of error. Further, in thisdetailed description, each numerical value (e.g., temperature or time)should be read once as modified by the term “about” (unless alreadyexpressly so modified), and then read again as not so modified unlessotherwise indicated in context. As noted, any range listed or describedherein is intended to include, implicitly or explicitly, any numberwithin the range, particularly all integers, including the end points,and is to be considered as having been so stated. For example, “a rangefrom 1 to 10” is to be read as indicating each possible number,particularly integers, along the continuum between about 1 and about 10.Thus, even if specific data points within the range, or even no datapoints within the range, are explicitly identified or specificallyreferred to, it is to be understood that any data points within therange are to be considered to have been specified, and that theinventors possessed knowledge of the entire range and the points withinthe range. The use of the term “about” may mean a range including ±10%of the subsequent number unless otherwise stated.

As used herein, the term “substantially” means that the subsequentlydescribed parameter, event, or circumstance completely occurs or thatthe subsequently described parameter, event, or circumstance occurs to agreat extent or degree. For example, the term “substantially” means thatthe subsequently described parameter, event, or circumstance occurs atleast 90% of the time, or at least 91%, or at least 92%, or at least93%, or at least 94%, or at least 95%, or at least 96%, or at least 97%,or at least 98%, or at least 99%, of the time, or means that thedimension or measurement is within at least 90%, or at least 91%, or atleast 92%, or at least 93%, or at least 94%, or at least 95%, or atleast 96%, or at least 97%, or at least 98%, or at least 99%, of thereferenced dimension or measurement (e.g., length).

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

The following abbreviations and initialisms apply:

ANN: artificial neural network

ASIC: application-specific integrated circuit

BPS: Bakken petroleum system

CNN: convolutional neural network

CPU: central processing unit

CT: computerized tomography

DSP: digital signal processor

EO: electrical-to-optical

FPGA: field-programmable gate array

ft: foot, feet

GAN: generative adversarial network

GHz: gigahertz

GR: gamma ray

GUI: graphical user interface

KL: Kullback-Leibler

LSTM: long short-term memory

MHz: megahertz

ML: machine learning

ms: millisecond(s)

mS/m: millisiemen(s) per meter

NMR: nuclear magnetic resonance

NN: neural network

OE: optical-to-electrical

RAM: random-access memory

RMS: root mean square

RNN: recurrent neural network

ROM: read-only memory

RX: receiver unit

SRAM: static RAM

TCAM: ternary content-addressable memory

TOC: total organic compound

TX: transmitter unit

VAE: variational auto-encoder

Vp: compressional velocity

Vs: shear velocity

wt %: percentage by weight

1D: one-dimensional

2D: two-dimensional

3D: three-dimensional

%: percentage.

The oil and gas industry is one industry that conducts material analysesof geological materials in laboratories and in the subsurface usingwireline logging tools, logging while drilling tools, measurement whiledrilling tools and rate/pressure transient measurements. For instance,the oil and gas industry performs spectral analyses of porous materialssuch as shale or other hydrocarbon-bearing rocks that contain water,carbon, or other materials to determine what kind of rock is analyzed,how porous the rock is, how big the pores are, a pore size distribution,a depth below the surface where the rock exhibits a desired property,whether water or oil is in the rock, how much water or oil is in therock, permeability, fluid mobility, bound fluid saturation, or othercharacteristics. The civil, mining, geoengineering, chemical,non-destructive testing, remote sensing, material science, analyticalchemistry, semiconductor, medical sensing, polymer, and geophysicalindustries also perform such material analyses. One way to conduct sucha material analysis is to perform spectroscopy.

Spectroscopy is a process in which a tool excites a material usingradiative energy such as electromagnetic radiation, kinetic energy ofparticles, acoustic waves, or mechanical impact and an instrumentmeasures a resulting spectral response or emission spectrum. Thespectral response is a signal produced due to an interaction between thematerial and the radiative energy. The instrument measures or decomposesthe signal as a function of a continuous variable such as energy inelectron spectroscopy, mass-to-charge ratio in mass spectroscopy,frequency in dielectric spectroscopy, relaxation time in NMRspectroscopy, or time in rate/pressure transient measurements.

For instance, NMR spectroscopy is a process in which a magnetic sourceapplies a magnetic field to a material and an instrument measures aresulting spectral response. Specifically, the magnetic sourcemagnetizes hydrogen nuclei, carbon nuclei, or other nuclei that havemagnetic poles. After application of the magnetic field, the materialundergoes relaxation, which is the return of the nuclei to theiroriginal states. As the material does so, the material emitselectromagnetic radiation in a specific manner. A device measures theelectromagnetic radiation as a spectral response and may determine a T2distribution from that spectral response. A T2 distribution indicates arelaxation time associated with broadening of a signal produced duringthe relaxation of the nuclei.

While spectroscopy is a helpful process, the cost of performingspectroscopy at a site of a material, such as a subsurface hydrocarbonreservoir, a groundwater aquifer, or deep mines, through a borehole maybe prohibitively high, particularly when the spectroscopy is NMRspectroscopy, dielectric spectroscopy, or elemental spectroscopy.Mohammad Mandi Labani, et al., “Estimation of NMR log parameters fromconventional well log data using a committee machine with intelligentsystems: A case study from the Iranian part of the South Pars gas field,Persian Gulf Basin,” Journal of Petroleum Science and Engineering, Vol.72, Issues 1-2, May 2010, which is incorporated by reference, describesa model that estimates NMR permeability from conventional logs. Themodel estimates NMR permeability, but not bound, free fluid, or otherkey data. Reza Farzi, et al., “Simulation of NMR response from micro-CTimages using artificial neural networks,” Journal of Natural Gas Scienceand Engineering, Vol. 39, pages 54-61, March 2017, which is incorporatedby reference, describes acquisition of micro-CT images throughmeasurements of core samples, extraction of numerical features from theCT images, and simulation of an NMR relation time using the numericalfeatures. However, the measurements of the core samples and theextraction of the numerical features are complex and applicable only ina laboratory.

Disclosed herein are embodiments for generating spectral responses ofmaterials. The embodiments may be performed in the absence of somespectroscopy tools by processing current measurements acquired usingavailable spectroscopy tools. The embodiments provide for training,testing, and generating steps based on machine learning and computationto produce generated, or synthetic or simulated, spectral responses ofmaterials using historical measurements for purposes of materialanalyses in the absence of a spectroscopy tool. The generated spectralresponses are not measured at a site, but are instead generated using agenerator, historical spectral responses, and other historicalmeasurements, thus greatly reducing the costs of analyzing materials.The embodiments dramatically reduce the dependency on spectroscopy toolswhen these tools are hard to find and deploy due to operational andlogistical challenges. The generated spectral responses help determinewhat kind of materials are analyzed, how porous the materials are, howbig the pores are, pore size distributions, a depth below the surfacewherein the materials exhibit desired properties, whether water or oilis in the materials, how much water or oil is in the materials,permeabilities, fluid mobilities, bound fluid saturations, internalstructures, compositions, concentrations, spatial variations, temporalchanges, or other characteristics of the materials. The embodimentsapply to generating spectral responses of subsurface geologicalformations such as shales, sandstones, carbonates, turbidites, and shalysands for the oil and gas industry, as well as generating spectralresponses of other materials for other industries.

A T2 distribution of a material is calculated as follows:

$\begin{matrix}{{\frac{1}{T_{2}} = {\frac{1}{T_{2,{bulk}}} + \frac{1}{T_{2,{surface}}} + \frac{1}{T_{2,{diffusion}}}}},} & (1)\end{matrix}$where T₂ is a T2 distribution of a material, T_(2,bulk) is a bulk fluidrelaxation indicating how much fluid is present in the material,T_(2,surface) is a surface relaxation indicating characteristics of asurface of the material, and T_(2,diffusion) is a diffusion relaxationindicating how excited nuclei diffuse within the material. A fluid type,a hydrogen content, and fluid mobility affect the bulk fluid relaxation.Surface relaxation occurs at a fluid-solid interface. A pore shape, porenetwork characteristics, and mineralogy affect surface relaxation.Diffusion relaxation occurs due to a non-zero gradient of a magneticfield exciting the material. Thus, both fluid saturations and chemicalcompositions of materials affect T2 distributions. By first determiningcomplex relationships between the fluid saturations and the chemicalcompositions on one hand and the T2 distributions on the other hand, aswell as identifying complex patterns and features in the spectra andmeasurements, one can then determine T2 distributions when receivingonly the fluid saturations and the chemical compositions as input data.Together, the fluid saturations, the chemical compositions, or othercharacteristics may be referred to as material measurements, historicalmeasurements, current measurements, real-time measurements, or simplymeasurements.

FIG. 1 is a schematic diagram of a system 100 for generating spectralresponses of materials. The system 100 comprises a controller 110, atrainer 120, a tester 130, a generator 140, and a database 150. Thecontroller 110 is a hardware computer server. The controller 110controls or instructs the trainer 120, the tester 130, and the generator140, so it may be understood that the controller 110 controls,instructs, or otherwise causes the functions of those components asdescribed below. Alternatively, the trainer 120, the tester 130, or thegenerator 140 act independently of the controller 110 for any of thefunctions of those components as described below. The controller 110 maycomprise a GUI or another interface in order for a user to instruct thecontroller 110 as to how to control the trainer 120, the tester 130, andthe generator 140. In addition, the user views or otherwise receivesdata from, or the user supplies data to, the trainer 120, the tester130, and the generator 140 via the GUI or other interface. The trainer120, the tester 130, and the generator 140 are described further below.The database 150 is a memory or a portion of a memory. The database 150stores data and supplies that data to the trainer 120, the tester 130,and the generator 140. Alternatively, the database 150 is separated intoa historical database storage coupled to the trainer 120 and the tester130, a current measurement storage coupled to the generator 140, and agenerated spectral response visualization and storage unit coupled tothe generator 140.

FIG. 2 is a flowchart illustrating a method 200 of generating a spectralresponse of a material according to an embodiment of the disclosure. Thesystem 100 performs the method 200. At step 210, training is performed.For instance, the trainer 120 performs the training in response toinstructions from the controller 110 to create a decoder and a neuralnetwork for step 220. The decoder is based on an iterative processing ofa historical spectral response of a historical material, and the neuralnetwork is based on an iterative processing of historical measurementsof the historical material. At step 220, testing is performed. Forinstance, the tester 130 performs the testing in response toinstructions from the controller 110 in order to assess a performance ofthe decoder and the neural network. The neural network and the decoderare based on a processing of testing measurements of the historicalmaterial. Finally, at step 230, generating is performed. For instance,the generator 140 performs the generating in response to instructionsfrom the controller 110 to produce a generated spectral response basedon a processing of current measurements of a current material for whichno measured spectral response is available, but for which a generatedspectral response is desired. The current material is the same materialas the historical material or is similar to the historical material. Thesteps 210, 220, 230 are described further below.

FIG. 3 is a schematic diagram of the trainer 120 in FIG. 1. The trainer120 generally comprises a deep neural network model 310 and a neuralnetwork system 360. As mentioned above, the trainer 120 performs step210 in FIG. 2.

The deep neural network model 310 may be a VAE, an LSTM, a CNN, an RNN,or a GAN. A VAE is a type of auto-encoder. The deep neural network model310 trains to reproduce its input data as its output data as describedin Ian Goodfellow, et al., “Deep Learning,” Nov. 10, 2016, which isincorporated by reference. Specifically, the deep neural network model310 trains to reproduce a historical spectral response as a reproducedspectral response. The deep neural network model 310 comprises anencoder 320, a latent space database 330, a decoder 340, and a comparer350. The encoder 320, the decoder 340, and the comparer 350 are softwarecomponents, and the latent space database 330 is a memory or a portionof a memory. The latent space database 330 may be a part of the database150.

The encoder 320 receives a historical spectral response from thedatabase 150, determines dominant features of the historical spectralresponse, encodes a latent space based on the dominant features, andprovides the latent space to the latent space database 330.Alternatively, the historical spectral response is a real-time spectralresponse, and the encoder 320 receives the real-time spectral responsefrom a real-time source of data. The historical spectral response is anNMR T2 distribution, an electromagnetic spectrum, a particleconcentration/energy spectrum, a natural or induced radiation spectrumsuch as a gamma-ray spectrum, or another suitable spectral response ofthe historical material. The historical spectral response originatesfrom a laboratory, subsurface, surface, or atmospheric environment. Thedominant features of the historical spectral response may be referred toas latent variables. The dominant features include heights of peaks,variances around the peaks, distances between the peaks, relativefeatures of the peaks, rates of change or other higher-order derivativesof magnitudes and phases with respect to time or other independentvariables, numbers of peaks, or other identifying features of thehistorical spectral response. The word “latent” connotes that thedominant features are not physical features of the historical spectralresponse with well-defined physical significance. The encoder 320constrains the latent variables to follow a Gaussian distribution asdescribed in Diederik Kingma, et al., “Auto-Encoding Variational Bayes,”May 1, 2014, which is incorporated by reference. The Gaussiandistribution adds uncertainty to the latent variables. The latent spaceis a collection of the latent variables organized in a manner thatindicates the latent variables and is therefore usable by the decoder340. Because the encoder 320 projects data from a relatively higherdimension in the historical spectral response to a relatively lowerdimension in the latent space, the encoder 320 narrows from left toright. Alternatively, the encoder acts as a creator or an extractor.

The latent space database 330 receives the latent space from the encoder320 and stores the latent space. In the latent space, data with similardominant features are close to each other in order to reduce a loss whenthe decoder 340 attempts to reproduce the historical spectral response.FIGS. 4 and 5 illustrate such a latent space.

FIG. 4 is a 2D latent space 400 according to an embodiment of thedisclosure. The 2D latent space 400 indicates latent variables. As afirst example, a top-left corner of the 2D latent space 400 comprisescurves with single peaks. As the curves shift to a top-middle portion ofthe 2D latent space 400, the peaks shift to the middle. As a secondexample, a top-right corner of the 2D latent space 400 comprises curveswith two peaks of similar amplitude. As the curves shift to abottom-right corner of the 2D latent space 400, left peaks lower inamplitude and right peaks increase in amplitude. The 2D latent space 400indicates other latent variables as well. These variations areindicative of the learning accomplished during the training the trainer120 performs at step 210.

FIG. 5 is a 3D latent space 500 according to an embodiment of thedisclosure. The 3D latent space 500 is similar to the 2D latent space400 in FIG. 4, but the 3D latent space 500 provides a third dimension oflatent variables in addition to the two dimensions of the 2D latentspace 400.

Returning to FIG. 3, the latent space in the latent space database 330is the 2D latent space 400, the 3D latent space 500, or another suitablelatent space. The latent space database 330 may store the 3D latentspace 500 or a higher-dimension latent space when there are manyhistorical spectral responses or when those historical responses have alarge range of latent variables.

The decoder 340 receives the latent space from the latent space database330 and decodes a reproduced spectral response based on the encoder'sprojection of the historical spectral response on the latent space.Because the decoder 340 projects data from a relatively lower dimensionin the latent space to a relatively higher dimension in the initialspectral response, the decoder 340 widens from left to right.Alternatively, the decoder 340 acts as a comparator or a reconstructor.

The comparer 350 compares the reproduced spectral response from thedecoder 340 to the historical spectral response at the encoder 320 todetermine a similarity between the reproduced spectral response and thehistorical spectral response. If the similarity is below an initialthreshold, then the comparer 350 instructs the encoder 320, the latentspace database 330, and the decoder 340 to repeat their functions, andthe comparer 350 reassigns weights and biases in the encoder 320 and thedecoder 340 until the similarity is at or above the initial threshold.Once the similarity is at or above the initial threshold, the comparer350 instructs the decoder 340 to freeze, and thus become a frozendecoder. The comparer 350 stores the decoder 340 for further use in theneural network system 360 in the trainer 120 and for further use in thetester 130.

The neural network system 360 comprises a neural network 370, a decoder380, and a comparer 390. The neural network 370 is a software componentor software components comprising various functions that train toproduce complex relationships between the historical spectral responseand the historical measurements. The neural network 370 may bedistributed among multiple hardware computers. The decoder 380 and thecomparer 390 are software components. The decoder 380 is the frozendecoder described above and remains frozen in the neural network system360. Thus, between the neural network 370 and the decoder 380, only theneural network 370 trains.

The neural network 370 receives historical measurements from thedatabase 150, determines dominant features of the historicalmeasurements, learns to relate the dominant features to the historicalspectral response, and provides the dominant features to the decoder380, thereby associating the historical measurements to the historicalspectral response. Alternatively, the historical measurements arereal-time measurements, and the neural network 370 receives thereal-time measurements from a real-time source of data. If thehistorical spectral response is an NMR T2 distribution, then thehistorical measurements comprise fluid saturation data; mineralcomposition data; raw subsurface logs such as gamma ray logs,resistivity logs, anisotropy logs, neutron porosity logs, densityporosity logs, or photoelectric factor logs; or other suitable data froma subsurface, a surface, the atmosphere, or a laboratory. If thespectral historical spectral response is a dielectric spectrum, then thehistorical measurements comprise natural radiation data, inducedradiation data, density data, porosity data, nuclear radiation data,photoelectric factor logs, sonic data, sonic logs, resistivity data, orother suitable data from a subsurface, a surface, the atmosphere, or alaboratory. The historical measurements are for the historical material.The dominant features of the historical measurements are heights ofpeaks, variances around the peaks, distances between the peaks, relativefeatures of the peaks, rates of change or other higher-order derivativesof magnitudes and phases with respect to time or other independentvariables, numbers of peaks, or other identifying features of thehistorical measurements.

The decoder 380 begins as the frozen decoder described above. Thedecoder 380 decodes a trained spectral response based on a projection ofthe historical measurements on the dominant features in the historicalmeasurements with respect to the historical spectral response. Becausethe decoder 380 projects data from a relatively lower dimension in thedominant features to a relatively higher dimension in the trainedspectral response, the decoder 380 widens from left to right.Alternatively, the decoder 340 is a comparator or a reconstructor.

The comparer 390 compares the trained spectral response from the decoder380 to the historical spectral response at the encoder 320 to determinea similarity between the trained spectral response and the historicalspectral response. If the similarity is below a final threshold, thenthe comparer 390 instructs the neural network 370 to repeat itsfunctions, and the comparer 390 reassigns weights and biases in theneural network 370 until the similarity is at or above the finalthreshold. Once the similarity is at or above the final threshold, thecomparer 390 instructs the neural network 370 to freeze, and thus becomea frozen neural network. The comparer 390 stores the neural network 370for further use in the tester 130.

FIG. 6 is a schematic diagram of mathematical operations performed in aVAE 600 according to an embodiment of the disclosure. The VAE 600 is anembodiment of the deep neural network model 310 in FIG. 3. The VAE 600comprises an input x 605, an encoder 610, function 615, a function 620,a loss function 625, a sample ε 630, a multiplier 635, an adder 640, alatent space z 645, a decoder 650, a function 655, and a loss function660.

The input x 605, the encoder 610, the latent space z 645, and thedecoder 650 are similar to the historical spectral response, the encoder320, the latent space in the latent space database 330, and the decoder340 in FIG. 3, respectively. The function 615 is an deterministicfunction that defines a mean of a probability distribution function of aprojection generated by the encoder 610. The function 620 is andeterministic function that defines a covariance the probabilitydistribution function. The function 655 is a deterministic function thatis a reproduction of the input x 605.

The loss functions 625, 660 guide the VAE 600 to generate an output, forinstance the reproduced spectral response in FIG. 3, that matches theinput x 605. The VAE 600 alters weights to minimize the loss functions625, 660. The loss function 660 enables the VAE 600 to learn to minimizea difference between inputs and outputs. A KL divergence of the lossfunction 625 forces the encoder 610 to generate the latent space z 645so that the latent space z 645 follows a Gaussian distribution. Asdescribed in Carl Doersch, “Tutorial on Variational Autoencoders,” Aug.13, 2016, which is incorporated by reference, a KL divergence betweentwo multivariate Gaussian distributions is expressed as follows:

$\begin{matrix}{{KL} = \left\lbrack {{{N\left( {{\mu(X)},\left. {\sum(x)}||{N\left( {0,I} \right)} \right.} \right\rbrack} = {\frac{1}{2}\left( {{{tr}\left( {\sum(X)} \right)} + {\left( {\mu(X)} \right)^{2}\left( {\mu(X)} \right)} - k - {\log\;{\det\left( {\sum(X)} \right)}}} \right)}},} \right.} & (2)\end{matrix}$where X is a historical spectral response and N(μ,Σ) represents theGaussian distribution with a mean μ a covariance Σ, and a dimensionalityk.

The sample ε 630 represents a re-parameterization trick to reconstructthe latent space z 645. A neural network such as the neural network 370in FIG. 3 trains through backpropagation and therefore calculates acontribution of each node to a final error, then learns to reduce anerror. If latent variables sample randomly from the Gaussiandistribution, then backpropagation may not be efficient for training theneural network. The representation process selects the sample ε 630 fromthe Gaussian distribution, which is N(0, I), and changes its mean andvariance through a linear calculation. By this method, the latent spacez 645 still follows the Gaussian distribution, but the neural networkmay train through backpropagation.

FIG. 7 is a schematic diagram of the tester 130 in FIG. 1. The tester130 is similar to the neural network system 360 in FIG. 3. However, thetester 130 comprises a neural network 710, which is the frozen neuralnetwork after the neural network system 360 completes training, and adecoder 720, which is the frozen decoder after the deep neural networkmodel 310 completes training. The tester 130 obtains the neural network710 and the decoder 720 from the database 150. As mentioned above, thetester 130 performs step 220 in FIG. 2.

The neural network 710 receives a testing spectral response and testingmeasurements from the database 150, determines dominant features of thetesting measurements that correspond to dominant features of the testingspectral response, and provides the dominant features of the testingmeasurements to the decoder 720. The testing spectral response and thetesting measurements are the same types of spectral response andmeasurements as the historical spectral response and historicalmeasurements described with respect to FIG. 3 and are therefore aspectral response and measurements of the historical material. Forinstance, the testing measurements may be measured at the same time asthe historical measurements, but may be separated from the historicalmeasurements randomly, arbitrarily, or based on specified criteria.Testing ensures limited or no overfitting.

The decoder 720 decodes a tested spectral response for testing based onthe dominant features from the neural network 710. The decoder 720stores the tested spectral response in the database 150 and provides thetested spectral response to the controller 110 or another component forcomparison to a spectral response corresponding to the testingmeasurements in order to determine the accuracy of the tester 130. Afterthe tester 130 completes testing, the tester 130 freezes the neuralnetwork 710 and the decoder 720 and stores them in the database 150 forfurther use in the generator 140. Alternatively, the decoder 340 is acomparator or a reconstructor.

FIG. 8 is a schematic diagram of the generator 140 in FIG. 1. Thegenerator 140 is similar to the tester 130 in FIG. 7. However, thegenerator 140 comprises a neural network 810, which is the frozen neuralnetwork after the neural network system 360 completes training and thetester 130 completes testing, and a decoder 820, which is the frozendecoder after the deep neural network model 310 completes training andthe tester 130 completes testing. The tester 130 obtains the neuralnetwork 810 and the decoder 820 from the database 150. As mentionedabove, the generator 140 performs step 230 in FIG. 2.

The neural network 810 receives current measurements from the database150, determines dominant features of the current measurements, andprovides the dominant features to the decoder 820. The currentmeasurements are of the current material. A tool or tools perform thecurrent measurements at a site of the current material. In this context,the word “measurement” and its derivatives indicate an actualmeasurement of a material by a device contacting or otherwise directlyobserving the material, while the word “generated” and its derivativesindicate a calculation of a spectral response of a material by a devicenot contacting or otherwise directly observing the material, but remotefrom and independent of the material. The current measurements are of afluid saturation, a gas saturation, a kerogen content, or a mineralcomposition. The fluid may be water or oil.

The decoder 820 decodes a generated spectral response based on thedominant features from the neural network 810. The decoder 820 storesthe generated spectral response in the database 150. The neural network810 and the decoder 820 need not perform their functions at the site ofthe current material, but may do so in a laboratory, a subsurface, oranother location remote from the current material. Alternatively, thedecoder 340 is a comparator or a reconstructor.

Though the components of the system 100 are described as being hardwarecomponents or software components, any suitable combination of hardwareor software may implement the components. In addition, the componentsmay exist on a single device, on multiple devices, or as distributedsensing and computing units. Furthermore, though the neural networksystem 360, the tester 130, and the generator 140 are described as threeseparate systems, they may be the same system, but at different stagessuch as training, testing, and simulating. Finally, though generatedspectral responses that are NMR T2 distributions and dielectric spectralresponses are described, the system 100 may generate other generatedspectral responses relevant to industries that performspectroscopy-based material analyses such as oil and gas, mining,material characterization, metallurgy, chemical, analytical chemistry,drug development, medical sensing, medical diagnostics, geophysical,remote sensing, semiconductor, construction, civil engineering, andother industries.

FIG. 9 is a flowchart illustrating a method 900 of generating agenerated spectral response of a material according to anotherembodiment of the disclosure. The generator 140 may implement the method900. At step 910, current measurements of a current material arereceived. For instance, the neural network 810 receives the currentmeasurements. At step 920, dominant features of the current measurementsare determined. For instance, the neural network 810 determines thedominant features. At step 930, the dominant features are provided. Forinstance, the neural network 810 provides the dominant features to thedecoder 820. Finally, at step 940, a generated spectral response of thecurrent material is generated based on the dominant features. Forinstance, the decoder 820 generates the generated spectral response.Based on the generated spectral response, one may determine what kind ofmaterial the current material is, how porous the current material is,how big the pores are, a pore size distribution, a depth below thesurface wherein the current material exhibits desired properties,whether water or oil is in the current material, how much water or oilis in the current material, a permeability, a fluid mobility, a boundfluid saturation, an internal structure, a composition, a concentration,a spatial variation, a temporal change, or another characteristic of thecurrent material. Based on that, one may determine whether to perform anoperation of a material area containing the current material and, if so,at what depth. The operation may be a drilling operation or a wellcompletion operation. The method 900 may implement other steps such ascomparing the dominant features of the current measurements withdominant features of historical measurements and relating the dominantfeatures of the current measurements to spectral responses in thedatabase 150.

FIG. 10 is a schematic diagram of a device 1000 according to anembodiment of the disclosure. The device 1000 may implement thedisclosed embodiments. For instance, the device 1000 implements thecontroller 110, the trainer 120, the tester 130, the generator 140, orthe database 150. The device 1000 comprises ingress ports 1010 and an RX1020 that receive data; a processor, logic unit, or CPU 1030 thatprocesses the data; a TX 1040 and egress ports 1050 that transmit thedata; and a memory 1060 that stores the data. The device 1000 may alsocomprise OE components and EO components coupled to the ingress ports1010, the RX 1020, the TX 1040, and the egress ports 1050 for ingress oregress of optical or electrical signals.

The processor 1030 is any suitable combination of hardware, middleware,firmware, or software. The processor 1030 comprises any combination ofone or more CPU chips, cores, FPGAs, ASICs, or DSPs. The processor 1030communicates with the ingress ports 1010, RX 1020, TX 1040, egress ports1050, and memory 1060. The processor 1030 comprises a spectral responsecomponent 1070, which implements the disclosed embodiments. Theinclusion of the spectral response component 1070 therefore provides asubstantial improvement to the functionality of the device 1000 andeffects a transformation of the device 1000 to a different state.Alternatively, the memory 1060 stores the spectral response component1070 as instructions, and the processor 1030 executes thoseinstructions.

The memory 1060 comprises one or more disks, tape drives, or solid-statedrives. The device 1000 may use the memory 1060 as an over-flow datastorage device to store programs when the device 1000 selects thoseprograms for execution and to store instructions and data that thedevice 1000 reads during execution of those programs. The memory 1060may be volatile or non-volatile and may be any combination of ROM, RAM,TCAM, or SRAM.

Example Training and Testing

The system 100 analyzed data from two formations in Bakken Formationshale. Specifically, the trainer 120 trained with the data and thetester 130 tested the data. Variations in mineral compositions changedpore structures, grain textures, and surface reflexivity of theformations. Those characteristics, as well as fluid saturations and porenetwork distribution, affected an NMR T2 distribution of the formations.

The data were randomly split into training data and testing data. Thetraining data comprised historical spectral responses and historicalmeasurements from 460 depths of the formations. The testing datacomprised testing measurements from 100 depths of the formations. Thehistorical spectral responses comprised 64 dimensions, and the encoder320 created both a 2D latent space and a 3D latent space.

For the 2D latent space, 100 randomly-selected samples are shown as the2D latent space 400 in FIG. 4. FIG. 11 is a plot 1100 comparinghistorical spectral responses to tested spectral responses for the 2Dlatent space. As shown, the historical spectral responses and the testedspectral responses are similar. FIG. 12 is a plot 1200 of R-squared forthe generated spectral responses for the 2D latent space. As shown, as anumber of data increased, the R-squared increased. For historicalspectral responses with one peak, the R-squared was 0.8, and anormalized RMS deviation was 14%. For historical spectral responses withtwo peaks, accuracy decreased. However, less than one-third of thehistorical spectral responses were associated with two peaks. An averageR-squared was 0.75, and an average normalized RMS deviation was 15%.

For the 3D latent space, the historical spectral responses and thetrained spectral responses were more similar. FIG. 13 is a plot 1300comparing historical spectral responses to tested spectral responses forthe 3D latent space. FIG. 14 is a plot 1400 of R-squared for thegenerated spectral responses for the 3D latent space. As shown, as anumber of data increased, the R-squared increased. An average R-squaredwas 0.77, which was better than for the 2D latent space, and an averagenormalized RMS deviation was 15%. However, the encoder 320 learnedunrepresentative features when implementing the 3D latent space. Forinstance, the distributions in the lower-left corner of the graph 1300exhibit second peaks, which are small, but unnatural features.

FIG. 15 is a series of plots 1500 comparing original dielectric spectrato generated dielectric spectra. The original dielectric spectra areshown with dashed lines, and the generated dielectric spectra are shownwith solid lines. The dielectric spectra comprise both conductivitydenoted as σ and relative permittivity denoted as ϵ_(r). The x-axesrepresent depth in ft, and the y-axes represent either conductivity inmS/m or relative permittivity without units. Both the conductivity andthe relative permittivity are functions of operating frequency. Theconductivity increases with an increase in operating frequency, and therelative permittivity decreases with an increase in operating frequency.The series of graphs 1500 comprises graphs of the conductivity and therelative permittivity acquired at operating frequencies f0, f1, f2, andf3 of a subsurface formation along a 300 ft depth interval of an oilwell drilled through a hydrocarbon-bearing shale formation, wherein 20MHz<f0<f1<f2<f3≅1 GHz. In the training phase, 3,434 depth points in theoil well were used to generate dielectric spectra usingnon-dielectric-spectra measurements such as gamma ray, density porosity,neutron porosity, formation photoelectric factor, bulk density, volumeof clay, delta-T compressional sonic, delta-T shear sonic, and laterologresistivity logs at various depths. In the testing phase, 300 depthpoints were used. Table 1 shows the accuracy of the tester 130 ingenerating dielectric spectra at f0, f1, f2, and f3 for the 300 depthpoints.

TABLE 1 Tester Accuracy R² f0 f1 f2 f3 Conductivity 0.93 0.92 0.90 0.86Permittivity 0.70 0.71 0.65 0.62

FIG. 16 is a GUI 1600 for implementing the system 100 in FIG. 1according to an embodiment of the disclosure. Specifically, the GUI 1600may implement the GUI described with respect to the controller 110 inFIG. 1. The GUI 1600 comprises a load inputs button 1610, a selectorgenerator button 1620, and a generate spectral response button 1630. Auser clicks or otherwise selects the load inputs button 1610 to uploadinputs like the current measurements in FIG. 8; clicks or otherwiseselects the select generator button 1620 to select a type of the trainer120, the tester 130, and the generator 140 in FIGS. 1, 3 and 7-8; andclicks or otherwise selects the generate spectral response button 1630to obtain the generated spectral response in FIG. 8. The generatedspectral response may be available in a subsequent GUI. The GUI 1600 andthe generator 140 may communicate via the cloud.

FIG. 17 is a schematic diagram of a generator 1700 based on a VAE-CNNmodel according to an embodiment of the disclosure. The generator 1700implements a VAE-CNN model. The generator 1700 comprises a first stage1710 that performs VAE with a convolution layer and a second stage 1720that is a VAE-based neural network. A VAE implements the CNN. The VAE isa neural network with a bottleneck architecture that compresseshigh-dimensional data into a low-dimensional space. The VAE comprises anintermediate layer with two or three neurons. The VAE is designed toreproduce its input data. The VAE moves a dataflow through a bottleneckfollowing a Gaussian distribution in the intermediate layer. A VAEccombines the VAE and a CNN. The VAEc trains using a two-stage procedure.A normal NMR T2 distribution has a smooth multi-Gaussian shape. In thefirst stage 1710, the VAEc trains to learn how to generate a typical NMRT2 from the training dataset. The VAEc processes NMR T2 data as a 1Ddatatype. A convolutional layer has 16 filters to transform the NMR T2and extract generalized features. An encoder constructs a 3D latentlayer after compressing the input T2 into the latent space, whilepreserving the relationship in a higher-dimensional space. A decodergenerates a typical NMR T2 from the 3D latent space. In the second stage1720, the trained decoder is an NMR T2 generator and trains with a smallneural network that processes a formation mineral composition and fluidsaturation measurements.

FIG. 18 is a schematic diagram of a generator 1800 based on an LSTM-RNNmodel according to an embodiment of the disclosure. The generator 1800implements an LSTM-RNN model. The generator 1800 comprises an encoder1810 and a decoder 1820. An LSTM is a recurrent neural network designedto handle long and short-term data dependencies such as those inlanguage and stock market prediction. The LSTM uses a repeated dataprocessing module to process data at each step. The LSTM memorizes thedata by internal state and keeps a data flow in the repeated dataprocessing module by gates, which are composed of sigmoid activationfunctions. The LSTM treats formation mineral content and fluidsaturation measurements and a 64-dimensional NMR T2 as sequences. TheLSTM mimics a language translation model, where an encoder uses inputlanguage to calculate an intermediate vector. A decoder decodes theintermediate vector into another language. The repeated data processingmodule solves a long-term data dependency in sequence-to-sequencemapping problems. The LSTM trained in one step as shown in FIG. 17. Theencoder encodes formation composition logs into a 15-length intermediatevector v. The decoder decodes the vector v into the 64-dimensional NMRT2.

FIG. 19 is a schematic diagram of a generator 1900 based on a GAN modelaccording to an embodiment of the disclosure. The generator 1900implements a GAN model. The generator 1900 comprises a first stage 1910and a second stage 1920. The training process of the generator 1900 issimilar to the training process of the generator 1700. In the firststage 1910, a GAN learns features in T2 distributions. A generator and adiscriminator train alternatively until the generator can generate anNMR T2 that cannot be identified by the discriminator as generated data.After the GAN trains, a frozen generator connects to a neural networklike in FIG. 3 to learn to associate different mineral contents andfluid saturations measurements with learned features of T2distributions. After the training, NMR T2 distributions are generated ina testing phase like in FIG. 3. T2 distributions generated by a GAN arenot smooth. Therefore, a Gaussian fitting process fits a generated NMRT2 to make it smoother and more realistic. The generator is a 3-layerneural network that receives a six-dimensional noise vector as an input.The generator learns to generate the NMR T2 with different shapes withthe aid of the discriminator. An output uses 64 dimensions to generatethe T2 distribution. The discriminator contains 4 layers with 2 hiddenlayers that have 64 and 16 neurons each. 4,608 and 5,234 parameterstrain in the generator and discriminator, respectively. After the firststage 1910, a third neural network comprises 4 layers having 2 hiddenlayers with 30 neurons each. A third neural network has 10-dimensionalinputs and 6-dimensional outputs. The third NN trains with the frozengenerator from the generator 1900 in a manner similar to the training inFIG. 3.

FIG. 20 is a flowchart illustrating a method 2000 of using a stacked ANNmodel for generating dielectric spectra according to an embodiment ofthe disclosure. The method 2000 may generate dielectric spectra,including conductivity spectra and relative permittivity spectra. 4conductivity and 4 permittivity spectra measurements are ranked using ak-fold cross validation method. Ranking is done in order of the accuracyof their predictions using an ANN model. After ranking 8 DD logs,another ANN model sequentially generates 8 dispersion spectra one at atime, starting with predicting a spectrum having a highest predictionaccuracy and ending with predicting a spectrum having a lowestprediction accuracy. During generation of each subsequent dispersionlog, the ANN specific to that log is fed with previously predicted logsand the 15 current measurements of current material for which thedielectric spectra is desired to be generated. Physics of chargepolarization gives rise to a dependence between conductivity andpermittivity at each frequency and to a dependence of conductivity andpermittivity dispersions on frequencies. The method 2000 mimics thesephysical trends. The 8 dielectric dispersion spectra are ranked in termsof prediction accuracy achieved when performing simultaneous generationusing a single neural network, then sequential predictions of the 8dielectric spectra are performed based on a predetermined rank, therebyhonoring the dependence of conductivity and permittivity dispersions onfrequencies.

While several embodiments have been provided in the present disclosure,it may be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, components, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and may be made without departing from the spirit and scopedisclosed herein.

What is claimed is:
 1. A controller comprising: a memory; and aprocessor coupled to the memory and configured to: cause a neuralnetwork to receive current measurements of a current material, whereinthe current measurements are non-spectral measurements, and wherein thecurrent material is a hydrocarbon-bearing geological rock in alaboratory or a subsurface; instruct the neural network to determinedominant features of the current measurements; instruct the neuralnetwork to provide the dominant features to a decoder; and instruct thedecoder to generate a generated spectral response of the currentmaterial based on the dominant features.
 2. The controller of claim 1,wherein the generated spectral response is independent of a measuredspectral response.
 3. The controller of claim 1, wherein the processoris further configured to further instruct the decoder to generate thegenerated spectral response independent of inputs other than thedominant features.
 4. The controller of claim 1, wherein the controlleris further configured to instruct, before causing the neural network toreceive the current measurements, a trainer to perform training using ahistorical spectral response of a historical material and usinghistorical measurements of the historical material.
 5. The controller ofclaim 4, wherein the controller is further configured to instruct, afterthe training but before causing the neural network to receive thecurrent measurements, a tester to perform testing using testingmeasurements and testing spectral responses of the historical material.6. The controller of claim 1, wherein the generated spectral response isa nuclear magnetic resonance (NMR) T2 distribution.
 7. The controller ofclaim 6, wherein the current measurements are of fluid saturation, a gassaturation, a kerogen content, or a mineral composition.
 8. Thecontroller of claim 6, wherein the current measurements are of a gammaray count, a sonic impedance, or a well-log response.
 9. The controllerof claim 1, wherein the generated spectral response is a dielectricspectrum.
 10. The controller of claim 9, wherein the currentmeasurements are of natural radiation, density, porosity, nuclearradiation, resistivity, sonic activity, or a combination thereof. 11.The controller of claim 1, wherein the generated spectral response is asynthetic spectral response.
 12. The controller of claim 1, wherein thecurrent measurements are of fluid saturation, a gas saturation, akerogen content, or a mineral composition.
 13. The controller of claim1, wherein the neural network implements a pattern-recognition code. 14.The controller of claim 1, wherein the decoder implements a neuralnetwork or a pattern-recognition code.
 15. The controller of claim 1,wherein the generated spectral response is a nuclear magnetic resonance(NMR) T1 distribution, a dielectric dispersion spectrum, or aconductivity spectrum.
 16. The controller of claim 1, wherein thecurrent measurements and the generated spectral response are acquired inthe laboratory or the subsurface.
 17. A method comprising: receivingcurrent measurements of a current material, wherein the currentmeasurements are non-spectral measurements, and wherein the currentmaterial is a hydrocarbon-bearing geological rock in a laboratory or asubsurface; determining dominant features of the current measurements;providing the dominant features; and generating a generated spectralresponse of the current material based on the dominant features.
 18. Themethod of claim 17, further comprising further generating the generatedspectral response independent of inputs other than the dominantfeatures, wherein the generated spectral response is independent of ameasured spectral response.
 19. The method of claim 17, furthercomprising performing, before receiving the current measurements,training using a historical spectral response of a historical materialand using historical measurements of the historical material.
 20. Themethod of claim 18, further comprising performing, after training butbefore receiving the current measurements, testing using testingmeasurements of the historical material.
 21. The method of claim 17,wherein the generated spectral response is a nuclear magnetic resonance(NMR) T2 distribution, and wherein the current measurements are of fluidsaturations, mineral compositions, or both fluid saturation and mineralcomposition.
 22. The method of claim 17, wherein the generated spectralresponse is a dielectric spectrum, and wherein the current measurementsare of natural radiation, density, porosity, nuclear radiation,resistivity, sonic activity, or a combination thereof.
 23. The method ofclaim 17, further comprising determining characteristics of the currentmaterial based on the generated spectral response.
 24. The method ofclaim 17, further comprising determining whether to perform an operationof a material area based on the characteristics, wherein the materialarea is a source of the current material.
 25. The method of claim 24,wherein the operation is a drilling operation.
 26. The method of claim24, wherein the operation is a well completion operation.
 27. The methodof claim 24, wherein the operation is a stimulation operation or aproduction operation.
 28. The method of claim 17, wherein the currentmeasurements are of fluid saturation, a gas saturation, a kerogencontent, or a mineral composition.
 29. The method of claim 17, whereinthe generated spectral response is a synthetic spectral response.
 30. Acomputer program product comprising instructions for storage on anon-transitory medium and that, when executed by a processor, cause anapparatus to: cause a neural network to receive current measurements ofa current material, wherein the current measurements are non-spectralmeasurements, and wherein the current material is a hydrocarbon-bearinggeological rock in a laboratory or a subsurface; instruct the neuralnetwork to determine dominant features of the current measurements;instruct the neural network to provide the dominant features to adecoder; and instruct the decoder to generate a generated spectralresponse of the current material based on the dominant features.
 31. Thecomputer program product of claim 28, wherein the generated spectralresponse is a nuclear magnetic resonance (NMR) T2 distribution.
 32. Thecomputer program product of claim 31, wherein the current measurementsare of fluid saturation, a gas saturation, a kerogen content, or amineral composition.